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"""Helper functions for advanced discretisations"""

from typing import List, Union

import numpy as np
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor


def extract_thresholds_from_dtree(
    dtree: Union[DecisionTreeClassifier, DecisionTreeRegressor],
    length_df: int,
) -> List[np.array]:
    """A helper function that extracts the decision threshold of a decision tree

    Args:
        dtree: A decisiontree model object
        length_df (int): length of the target dataframe

    Returns:
        a list of numpy array indicating the thersholds for each feature
    """

    tree_threshold = dtree.tree_.threshold
    tree_feature = dtree.tree_.feature

    # store decision thresholds of all features in a list
    thresholds_for_features = []

    for feat in range(length_df):
        if feat not in tree_feature:
            thresholds_for_features.append(np.array([]))
        else:
            thresholds_for_features.append(
                np.unique(tree_threshold[tree_feature == feat])
            )
    return thresholds_for_features
